A New Country for Self-Driving Cars

Intel-owned Mobileye has announced they have a fleet of 100 self-driving vehicles on the roads in Jerusalem. That’s a lot of cars.

Israel has been a hotbed of autonomous vehicle development, but Israeli automotive companies face the same problem that almost all Israeli companies must surmount — a small local market. Mobileye has grown into a world leader in computer vision by supplying its perception systems to large automotive manufacturers outside of Israel.

Nonetheless, Mobileye founder and CEO Amnon Shashua writes that testing in Israel is important because of the challenges of Israeli driving. The title of the post is, “If You Can Drive in Jerusalem You Can Drive (Almost) Anywhere”.

“Jerusalem is notorious for aggressive driving. There aren’t perfectly marked roads. And there are complicated merges. People don’t always use crosswalks. You can’t have an autonomous car traveling at an overly cautious speed, congesting traffic or potentially causing an accident. You must drive assertively and make quick decisions like a local driver.”

Mobileye also published a video showing its self-driving cars executing several lane changes and other maneuvers in heavy traffic. The accompanying description of how the Mobileye vehicle is signaling to other cars on the road is impressive.

I was particularly struck by the description of Mobileye’s planning function.

“The part of our driving policy system that proposes actions is trained offline to optimize an assertive, smooth and human-like driving style. This is a proprietary software developed using artificial intelligence-based reinforcement learning [DS: my emphasis] techniques. This system is the largest advancement demonstrated in the fleet, and you can see the impressive results in the event visuals.”

Reinforcement learning has led to tremendous breakthroughs in games like chess and Go. It has stalled in its ability to translate to the real world, where simulation is less high-fidelity. This marks one of the first successful deployments of reinforcement learning to physical world problems.

Lots of neat stuff here.